1 Introduction

1.1 Getting Start

plotly is an R package for creating interactive web-based graphs via the open source JavaScript graphing library plotly.js. As of version 2.0 (November 17, 2015), graphs created with the plotly R package are rendered locally through the htmlwidgets framework.

1.2 Installation

1.2.1 CRAN

Use the install.package() function to install the plotly R package from CRAN. This version may not be the absolute latest version, so we recommend downloading from Github using the instructions below if you can.

install.packages('plotly')

1.2.2 GitHub

Alternatively, you can install the latest development version of plotly from GitHub via the devtools R package:

devtools::install_github("ropensci/plotly")

2 Basic Charts

2.1 Scatter Plots

2.1.1 Basic Scatter Plot

library(plotly)

fig <- plot_ly(data = mtcars, x = ~hp, y= ~mpg, type = "scatter", mode = "markers")
fig

2.1.2 Styled Scatter Plots

library(plotly)

fig <- plot_ly(data = mtcars, x = ~hp, y= ~mpg, type = "scatter",
               mode = "markers"，
               marker = list(size = 10,
                             color = 'rgba(255, 182, 193, .9)',
                             line = list(color = 'rgba(152, 0, 0, .8)',
                                         width = 2)))
fig <- fig %>% layout(title = 'Styled Scatter',
         yaxis = list(zeroline = FALSE),
         xaxis = list(zeroline = FALSE))

fig

2.1.3 Qualitative Colorscales

library(plotly)
fig <- plot_ly(data = mtcars, x = ~hp, y = ~mpg, color = ~cyl)
fig

2.2 Line Plots

2.2.1 Basic Line Plot

library(plotly)

x <- c(1:100)
random_y <- rnorm(100, mean = 0)
data <- data.frame(x, random_y)

fig <- plot_ly(data, x = ~x, y = ~random_y, type = 'scatter', mode = 'lines')

fig

2.2.2 Line Plots Mode

library(plotly)

trace_0 <- rnorm(100, mean = 5)
trace_1 <- rnorm(100, mean = 0)
trace_2 <- rnorm(100, mean = -5)
x <- c(1:100)

data <- data.frame(x, trace_0, trace_1, trace_2)

fig <- plot_ly(data, x = ~x, y = ~trace_0, name = 'trace 0', type = 'scatter', mode = 'lines') 
fig <- fig %>% add_trace(y = ~trace_1, name = 'trace 1', mode = 'lines+markers') 
fig <- fig %>% add_trace(y = ~trace_2, name = 'trace 2', mode = 'markers')

fig

2.2.3 Density Plot

library(plotly)

dens <- with(diamonds, tapply(price, INDEX = cut, density))
df <- data.frame(
  x = unlist(lapply(dens, "[[", "x")),
  y = unlist(lapply(dens, "[[", "y")),
  cut = rep(names(dens), each = length(dens[[1]]$x))
)

fig <- plot_ly(df, x = ~x, y = ~y, color = ~cut) 
fig <- fig %>% add_lines()

fig

2.3 Bar Charts

2.3.1 Basic Bar Chart

library(plotly)

fig <- plot_ly(
  x = c("giraffes", "orangutans", "monkeys"),
  y = c(20, 14, 23),
  name = "SF Zoo",
  type = "bar"
)

fig

2.3.2 Grouped Bar Chart

library(plotly)

Animals <- c("giraffes", "orangutans", "monkeys")
SF_Zoo <- c(20, 14, 23)
LA_Zoo <- c(12, 18, 29)
data <- data.frame(Animals, SF_Zoo, LA_Zoo)

fig <- plot_ly(data, x = ~Animals, y = ~SF_Zoo, type = 'bar', name = 'SF Zoo')
fig <- fig %>% add_trace(y = ~LA_Zoo, name = 'LA Zoo')
fig <- fig %>% layout(yaxis = list(title = 'Count'), barmode = 'group')

fig

2.3.3 Stacked Bar Chart

library(plotly)

Animals <- c("giraffes", "orangutans", "monkeys")
SF_Zoo <- c(20, 14, 23)
LA_Zoo <- c(12, 18, 29)
data <- data.frame(Animals, SF_Zoo, LA_Zoo)

fig <- plot_ly(data, x = ~Animals, y = ~SF_Zoo, type = 'bar', name = 'SF Zoo')
fig <- fig %>% add_trace(y = ~LA_Zoo, name = 'LA Zoo')
fig <- fig %>% layout(yaxis = list(title = 'Count'), barmode = 'stack')

fig

2.3.4 Colored and Styled Bar Chart

library(plotly)

x <- c(1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012)
roW <- c(219, 146, 112, 127, 124, 180, 236, 207, 236, 263, 350, 430, 474, 526, 488, 537, 500, 439)
China <- c(16, 13, 10, 11, 28, 37, 43, 55, 56, 88, 105, 156, 270, 299, 340, 403, 549, 499)
data <- data.frame(x, roW, China)

fig <- plot_ly(data, x = ~x, y = ~roW, type = 'bar', name = 'Rest of the World',
        marker = list(color = 'rgb(55, 83, 109)'))
fig <- fig %>% add_trace(y = ~China, name = 'China', marker = list(color = 'rgb(26, 118, 255)'))
fig <- fig %>% layout(title = 'US Export of Plastic Scrap',
         xaxis = list(
           title = "",
           tickfont = list(
             size = 14,
             color = 'rgb(107, 107, 107)')),
         yaxis = list(
           title = 'USD (millions)',
           titlefont = list(
             size = 16,
             color = 'rgb(107, 107, 107)'),
           tickfont = list(
             size = 14,
             color = 'rgb(107, 107, 107)')),
         legend = list(x = 0, y = 1, bgcolor = 'rgba(255, 255, 255, 0)', bordercolor = 'rgba(255, 255, 255, 0)'),
         barmode = 'group', bargap = 0.15, bargroupgap = 0.1)

fig

2.4 Pie Charts

2.4.1 Basic Pie Chart

library(plotly)

USPersonalExpenditure <- data.frame("Categorie"=rownames(USPersonalExpenditure), USPersonalExpenditure)
data <- USPersonalExpenditure[,c('Categorie', 'X1960')]

fig <- plot_ly(data, labels = ~Categorie, values = ~X1960, type = 'pie')
fig <- fig %>% layout(title = 'United States Personal Expenditures by Categories in 1960',
         xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),
         yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE))

fig

2.4.2 Donut Chart

library(plotly)
library(dplyr)

# Get Manufacturer
mtcars$manuf <- sapply(strsplit(rownames(mtcars), " "), "[[", 1)

df <- mtcars
df <- df %>% group_by(manuf)
df <- df %>% summarize(count = n())
fig <- df %>% plot_ly(labels = ~manuf, values = ~count)
fig <- fig %>% add_pie(hole = 0.6)
fig <- fig %>% layout(title = "Donut charts using Plotly",  showlegend = F,
                      xaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE),
                      yaxis = list(showgrid = FALSE, zeroline = FALSE, showticklabels = FALSE))

fig

2.5 Bubble Charts

2.5.1 Simple Bubble Chart

library(plotly)

data <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/school_earnings.csv")

fig <- plot_ly(data, x = ~Women, y = ~Men, text = ~School, type = 'scatter', mode = 'markers',
        marker = list(size = ~Gap, opacity = 0.5))
fig <- fig %>% layout(title = 'Gender Gap in Earnings per University',
         xaxis = list(showgrid = FALSE),
         yaxis = list(showgrid = FALSE))

fig

2.5.2 Mapping a Color Variable (Continuous)

library(plotly)

data <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/school_earnings.csv")

fig <- plot_ly(data, x = ~Women, y = ~Men, text = ~School, type = 'scatter', mode = 'markers', color = ~Gap, colors = 'Reds',
        marker = list(size = ~Gap, opacity = 0.5))
fig <- fig %>% layout(title = 'Gender Gap in Earnings per University',
         xaxis = list(showgrid = FALSE),
         yaxis = list(showgrid = FALSE))

fig

2.5.3 Mapping a Color Variable (Categorical)

library(plotly)

data <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/school_earnings.csv")

data$State <- as.factor(c('Massachusetts', 'California', 'Massachusetts', 'Pennsylvania', 'New Jersey', 'Illinois', 'Washington DC',
                          'Massachusetts', 'Connecticut', 'New York', 'North Carolina', 'New Hampshire', 'New York', 'Indiana',
                          'New York', 'Michigan', 'Rhode Island', 'California', 'Georgia', 'California', 'California'))

fig <- plot_ly(data, x = ~Women, y = ~Men, text = ~School, type = 'scatter', mode = 'markers', size = ~Gap, color = ~State, colors = 'Paired',
        marker = list(opacity = 0.5, sizemode = 'diameter'))
fig <- fig %>% layout(title = 'Gender Gap in Earnings per University',
         xaxis = list(showgrid = FALSE),
         yaxis = list(showgrid = FALSE),
         showlegend = FALSE)

fig

2.5.4 Styled Buble Chart

library(plotly)

data <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/gapminderDataFiveYear.csv")

data_2007 <- data[which(data$year == 2007),]
data_2007 <- data_2007[order(data_2007$continent, data_2007$country),]
slope <- 2.666051223553066e-05
data_2007$size <- sqrt(data_2007$pop * slope)
colors <- c('#4AC6B7', '#1972A4', '#965F8A', '#FF7070', '#C61951')

fig <- plot_ly(data_2007, x = ~gdpPercap, y = ~lifeExp, color = ~continent, size = ~size, colors = colors,
        type = 'scatter', mode = 'markers', sizes = c(min(data_2007$size), max(data_2007$size)),
        marker = list(symbol = 'circle', sizemode = 'diameter',
                      line = list(width = 2, color = '#FFFFFF')),
        text = ~paste('Country:', country, '<br>Life Expectancy:', lifeExp, '<br>GDP:', gdpPercap,
                      '<br>Pop.:', pop))
fig <- fig %>% layout(title = 'Life Expectancy v. Per Capita GDP, 2007',
         xaxis = list(title = 'GDP per capita (2000 dollars)',
                      gridcolor = 'rgb(255, 255, 255)',
                      range = c(2.003297660701705, 5.191505530708712),
                      type = 'log',
                      zerolinewidth = 1,
                      ticklen = 5,
                      gridwidth = 2),
         yaxis = list(title = 'Life Expectancy (years)',
                      gridcolor = 'rgb(255, 255, 255)',
                      range = c(36.12621671352166, 91.72921793264332),
                      zerolinewidth = 1,
                      ticklen = 5,
                      gridwith = 2),
         paper_bgcolor = 'rgb(243, 243, 243)',
         plot_bgcolor = 'rgb(243, 243, 243)')

fig

2.6 Sankey Diagram

2.6.1 Basic Sankey Diagram

library(plotly)

fig <- plot_ly(
    type = "sankey",
    orientation = "h",

    node = list(
      label = c("A1", "A2", "B1", "B2", "C1", "C2"),
      color = c("blue", "blue", "blue", "blue", "blue", "blue"),
      pad = 15,
      thickness = 20,
      line = list(
        color = "black",
        width = 0.5
      )
    ),

    link = list(
      source = c(0,1,0,2,3,3),
      target = c(2,3,3,4,4,5),
      value =  c(8,4,2,8,4,2)
    )
  )
fig <- fig %>% layout(
    title = "Basic Sankey Diagram",
    font = list(
      size = 10
    )
)

fig

2.6.2 Style Sankey Diagram

library(plotly)
library(rjson)

json_file <- "https://raw.githubusercontent.com/plotly/plotly.js/master/test/image/mocks/sankey_energy_dark.json"
json_data <- fromJSON(paste(readLines(json_file), collapse=""))

fig <- plot_ly(
    type = "sankey",
    domain = list(
      x =  c(0,1),
      y =  c(0,1)
    ),
    orientation = "h",
    valueformat = ".0f",
    valuesuffix = "TWh",

    node = list(
      label = json_data$data[[1]]$node$label,
      color = json_data$data[[1]]$node$color,
      pad = 15,
      thickness = 15,
      line = list(
        color = "black",
        width = 0.5
      )
    ),

    link = list(
      source = json_data$data[[1]]$link$source,
      target = json_data$data[[1]]$link$target,
      value =  json_data$data[[1]]$link$value,
      label =  json_data$data[[1]]$link$label
    )
  )
fig <- fig %>% layout(
    title = "Energy forecast for 2050<br>Source: Department of Energy & Climate Change, Tom Counsell via <a href='https://bost.ocks.org/mike/sankey/'>Mike Bostock</a>",
    font = list(
      size = 10,
      color = 'white'
    ),
    xaxis = list(showgrid = F, zeroline = F, showticklabels = F),
    yaxis = list(showgrid = F, zeroline = F, showticklabels = F),
    plot_bgcolor = 'black',
    paper_bgcolor = 'black'
)

fig

3 Statistical Charts

3.1 2D Histograms

A 2D histogram is a visualization of a bivariate distribution. And it looks like a heat map.

#
library(plotly)

s <- matrix(c(1, -.75, -.75, 1), ncol = 2)
obs <- mvtnorm::rmvnorm(500, sigma = s)
fig <- plot_ly(data = mtcars, x = ~hp, y= ~mpg)
fig2 <- subplot(
  fig %>% add_markers(alpha = 0.2),
  fig %>% add_histogram2d()
)

fig2

3.2 Box Plot

3.2.1 Basic Boxplot

library(plotly)
fig <- plot_ly(data = mtcars, y= ~mpg, type = "box")

fig

3.2.2 Horizontal Boxplot

library(plotly)
fig <- plot_ly(data = mtcars, x= ~mpg, type = "box")

fig

3.2.3 Adding Jittered Points

library(plotly)
fig <- plot_ly(data = mtcars, y= ~mpg, type = "box", boxpoints = "all", jitter = 0.3)

fig

3.2.4 Several Box Plots

fig <- plot_ly(data = mtcars, y= ~mpg, color = ~as.factor(cyl), type = "box")

fig

3.3 Histograms

3.3.1 Basic Histograms

library(plotly)
fig <- plot_ly(data = mtcars, x = ~hp, type = "histogram", nbinsx = 15)

fig

3.3.2 Stacked Histogram

fig <- plot_ly(data = mtcars, x = ~hp,
             type = "histogram",
             cumulative = list(enabled=TRUE),
             nbinsx = 15)

fig

3.4 Violin Plots

fig <- mtcars %>%
  plot_ly(
    y = ~hp,
    type = 'violin',
    box = list(
      visible = T
    ),
    meanline = list(
      visible = T
    ),
    x0 = 'Horse Power'
  ) 

fig <- fig %>%
  layout(
    yaxis = list(
      title = "",
      zeroline = F
    )
  )

fig

4 Scientific Charts

4.1 Log Plots

4.1.1 Log Axes

library(plotly)
d <- diamonds[sample(nrow(diamonds), 1000), ]
# without log scales
fig <- plot_ly(d, x = ~carat, y = ~price) %>% add_markers()

fig

# with log scales
fig <- layout(fig, xaxis = list(type = "log"),
       yaxis = list(type = "log"))

fig

4.2 Contour Plots

4.2.1 Basic Contour

library(plotly)

fig <- plot_ly(z = ~volcano, type = "contour")

fig

4.2.2 Smoothing Contour Coloring

library(plotly)

fig <- plot_ly(
  type = 'contour',
  z = matrix(c(10, 10.625, 12.5, 15.625, 20, 5.625, 6.25, 8.125, 
               11.25, 15.625, 2.5, 3.125, 5, 8.125, 12.5, 0.625, 
               1.25, 3.125, 6.25, 10.625, 0, 0.625, 2.5, 5.625, 
               10), nrow=5, ncol=5),
  contours = list(
    coloring = 'heatmap'
  )
)

fig

4.3 Heatmaps

4.3.1 Basic Heatmap

library(plotly)
fig <- plot_ly(z = volcano, type = "heatmap")

fig

4.3.2 Sequential Colorscales: Greys

fig <- plot_ly(z = volcano, colors = "Greys", type = "heatmap")

fig

4.4 Parallel Coordinates

4.4.1 Basic Parallel Cordinates Plots

library(plotly)

df <- read.csv("https://raw.githubusercontent.com/bcdunbar/datasets/master/iris.csv")

fig <- df %>% plot_ly(type = 'parcoords',
          line = list(color = ~species_id,
                      colorscale = list(c(0,'red'),c(0.5,'green'),c(1,'blue'))),
          dimensions = list(
            list(range = c(2,4.5),
                 label = 'Sepal Width', values = ~sepal_width),
            list(range = c(4,8),
                 constraintrange = c(5,6),
                 label = 'Sepal Length', values = ~sepal_length),
            list(range = c(0,2.5),
                 label = 'Petal Width', values = ~petal_width),
            list(range = c(1,7),
                 label = 'Petal Length', values = ~petal_length)
            )
          )

fig

4.4.2 Advanced Parallel Coordinates Plot

library(plotly)

df <- read.csv("https://raw.githubusercontent.com/bcdunbar/datasets/master/parcoords_data.csv")

fig <- df %>%
  plot_ly(width = 1000, height = 600) 
fig <- fig %>% add_trace(type = 'parcoords',
          line = list(color = ~colorVal,
                      colorscale = 'Jet',
                      showscale = TRUE,
                      reversescale = TRUE,
                      cmin = -4000,
                      cmax = -100),
          dimensions = list(
            list(range = c(~min(blockHeight),~max(blockHeight)),
                 constraintrange = c(100000,150000),
                 label = 'Block Height', values = ~blockHeight),
            list(range = c(~min(blockWidth),~max(blockWidth)),
                 label = 'Block Width', values = ~blockWidth),
            list(tickvals = c(0,0.5,1,2,3),
                 ticktext = c('A','AB','B','Y','Z'),
                 label = 'Cyclinder Material', values = ~cycMaterial),
            list(range = c(-1,4),
                 tickvals = c(0,1,2,3),
                 label = 'Block Material', values = ~blockMaterial),
            list(range = c(~min(totalWeight),~max(totalWeight)),
                 visible = TRUE,
                 label = 'Total Weight', values = ~totalWeight),
            list(range = c(~min(assemblyPW),~max(assemblyPW)),
                 label = 'Assembly Penalty Weight', values = ~assemblyPW),
            list(range = c(~min(HstW),~max(HstW)),
                 label = 'Height st Width', values = ~HstW),
            list(range = c(~min(minHW),~max(minHW)),
                 label = 'Min Height Width', values = ~minHW),
            list(range = c(~min(minWD),~max(minWD)),
                 label = 'Min Width Diameter', values = ~minWD),
            list(range = c(~min(rfBlock),~max(rfBlock)),
                 label = 'RF Block', values = ~rfBlock)
            )
          )


fig

4.5 Polar Charts

4.5.1 Basic Polar Charts

library(plotly)

fig <- plot_ly(
  type = 'scatterpolar',
  r = c(0,1,2,2),
  theta = c(0,45,90,0),
  mode = 'markers'
)

fig

4.5.2 Line Polar Charts

library(plotly)

df <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/polar_dataset.csv")

fig <- plot_ly(
  df,
  type = 'scatterpolar',
  mode = 'lines'
  ) 
fig <- fig %>%
  add_trace(
    r = ~x1,
    theta = ~y,
    name = 'Figure8',
    line = list(
      color = 'peru'
    )
  ) 
fig <- fig %>%
  add_trace(
    r = ~x2,
    theta = ~y,
    name = 'Cardioid',
    line = list(
      color = 'darkviolet'
    )
  ) 
fig <- fig %>%
  add_trace(
    r = ~x3,
    theta = ~y,
    name = 'Hypercardioid',
    line = list(
      color = 'deepskyblue'
    )
  ) 
fig <- fig %>%
  add_trace(
    r = ~x4,
    theta = ~y,
    name = 'Subcardioid',
    line = list(
      color = 'orangered'
    )
  ) 
fig <- fig %>%
  add_trace(
    r = ~x5,
    theta = ~y,
    name = 'Supercardioid',
    line = list(
      color = 'green'
    )
  ) 

fig <- fig %>%
  layout(
    title = 'Mic Patterns',
    font = list(
      family = 'Arial',
      size = 12,
      color = '#000'
    ),
    showlegend = F
  )

fig

4.5.3 Area Polar Area

library(plotly)

fig <- plot_ly(
    type = 'scatterpolar',
    mode = 'lines'
  ) 
fig <- fig %>%
  add_trace(
    r = c(0, 1.5, 1.5, 0, 2.5, 2.5, 0),
    theta = c(0, 10, 25, 0, 205, 215, 0),
    fill = 'toself',
    fillcolor = '#709Bff',
    line = list(
      color = 'black'
    )
  ) 
fig <- fig %>%
  add_trace(
    r = c(0, 3.5, 3.5, 0),
    theta = c(0, 55, 75, 0),
    fill = 'toself',
    fillcolor = '#E4FF87',
    line = list(
      color = 'black'
    )
  ) 
fig <- fig %>%
  add_trace(
    r = c(0, 4.5, 4.5, 0, 4.5, 4.5, 0),
    theta = c(0, 100, 120, 0, 305, 320, 0),
    fill = 'toself',
    fillcolor = '#FFAA70',
    line = list(
      color = 'black'
    )
  ) 
fig <- fig %>%
  add_trace(
    r = c(0, 4, 4, 0),
    theta = c(0, 165, 195, 0),
    fill = 'toself',
    fillcolor = '#FFDF70',
    line = list(
      color = 'black'
    )
  ) 
fig <- fig %>%
  add_trace(
    r = c(0, 3, 3, 0),
    theta = c(0, 262.5, 277.5, 0),
    fill = 'toself',
    fillcolor = '#B6FFB4',
    line = list(
      color = 'black'
    )
  ) 
fig <- fig %>%
  layout(
    polar = list(
      radialaxis = list(
        visible = T,
        range = c(0,5)
      )
    ),
    showlegend = F
  )

fig

4.6 Radar Charts

4.6.1 Basic Radar Charts

library(plotly)

fig <- plot_ly(
    type = 'scatterpolar',
    r = c(39, 28, 8, 7, 28, 39),
    theta = c('A','B','C', 'D', 'E', 'A'),
    fill = 'toself'
  ) 
fig <- fig %>%
  layout(
    polar = list(
      radialaxis = list(
        visible = T,
        range = c(0,50)
      )
    ),
    showlegend = F
  )

fig

4.6.2 Multiple Trace Radar Charts

library(plotly)

fig <- plot_ly(
    type = 'scatterpolar',
    fill = 'toself'
  ) 
fig <- fig %>%
  add_trace(
    r = c(39, 28, 8, 7, 28, 39),
    theta = c('A','B','C', 'D', 'E', 'A'),
    name = 'Group A'
  ) 
fig <- fig %>%
  add_trace(
    r = c(1.5, 10, 39, 31, 15, 1.5),
    theta = c('A','B','C', 'D', 'E', 'A'),
    name = 'Group B'
  ) 
fig <- fig %>%
  layout(
    polar = list(
      radialaxis = list(
        visible = T,
        range = c(0,50)
      )
    )
  )

fig

5 Financial Charts

5.1 Time Series and Date Axess

5.1.1 Time Series using Axes of type date

library(plotly)

stock <- read.csv('https://raw.githubusercontent.com/plotly/datasets/master/finance-charts-apple.csv')

fig <- plot_ly(stock, type = 'scatter', mode = 'lines')%>%
  add_trace(x = ~Date, y = ~AAPL.High)%>%
  layout(showlegend = F)
fig <- fig %>%
  layout(
         xaxis = list(zerolinecolor = '#ffff',
                      zerolinewidth = 2,
                      gridcolor = 'ffff'),
         yaxis = list(zerolinecolor = '#ffff',
                      zerolinewidth = 2,
                      gridcolor = 'ffff'),
         plot_bgcolor='#e5ecf6', width = 900)


fig

5.1.2 Configuring Tick Labels

library(tidyquant)
library(plotly)
tickers = c("GOOG", "AAPL", "AMZN", "META", "NFLX", "MSFT")
for (i in tickers){
  getSymbols(i,
             from = "2020-01-01",
             to = "2021-12-31")}
stock <- data.frame(GOOG$GOOG.Adjusted,
                    AAPL$AAPL.Adjusted,
                    AMZN$AMZN.Adjusted,
                    META$META.Adjusted,
                    NFLX$NFLX.Adjusted,
                    MSFT$MSFT.Adjusted)
stock$GOOG.Adjusted <- stock$GOOG.Adjusted/stock$GOOG.Adjusted[1]
stock$AAPL.Adjusted <- stock$AAPL.Adjusted/stock$AAPL.Adjusted[1]
stock$AMZN.Adjusted <- stock$AMZN.Adjusted/stock$AMZN.Adjusted[1]
stock$META.Adjusted <- stock$META.Adjusted/stock$META.Adjusted[1]
stock$NFLX.Adjusted <- stock$NFLX.Adjusted/stock$NFLX.Adjusted[1]
stock$MSFT.Adjusted <- stock$MSFT.Adjusted/stock$MSFT.Adjusted[1]
stock <- data.frame(stock,rownames(stock))
colnames(stock) <- append(tickers,'Dates')

fig <- plot_ly(stock, type = 'scatter', mode = 'lines')%>%
  add_trace(x = ~Dates, y = ~GOOG, name = 'GOOG')%>%
  add_trace(x = ~Dates, y = ~AAPL, name = 'AAPL')%>%
  add_trace(x = ~Dates, y = ~AMZN, name = 'AMZN')%>%
  add_trace(x = ~Dates, y = ~META, name = 'META')%>%
  add_trace(x = ~Dates, y = ~NFLX, name = 'NFLX')%>%
  add_trace(x = ~Dates, y = ~MSFT, name = 'MSFT')%>%
  layout(title = 'custom tick labels',legend=list(title=list(text='variable')),
         xaxis = list(dtick = "M1", tickformat="%b<br>%Y"), width = 1000)
options(warn = -1)
fig <- fig %>%
  layout(
         xaxis = list(zerolinecolor = '#ffff',
                      zerolinewidth = 2,
                      gridcolor = 'ffff'),
         yaxis = list(zerolinecolor = '#ffff',
                      zerolinewidth = 2,
                      gridcolor = 'ffff'),
         plot_bgcolor='#e5ecf6')


fig

5.1.3 Time Series with Range Selector Buttons

library(plotly)

stock <- read.csv('https://raw.githubusercontent.com/plotly/datasets/master/finance-charts-apple.csv')

fig <- plot_ly(stock, type = 'scatter', mode = 'lines')%>%
  add_trace(x = ~Date, y = ~AAPL.High)%>%
  layout(showlegend = F, title='Time Series with Range Slider and Selectors',
         xaxis = list(rangeslider = list(visible = T),
                      rangeselector=list(
                        buttons=list(
                          list(count=1, label="1m", step="month", stepmode="backward"),
                          list(count=6, label="6m", step="month", stepmode="backward"),
                          list(count=1, label="YTD", step="year", stepmode="todate"),
                          list(count=1, label="1y", step="year", stepmode="backward"),
                          list(step="all")
                        ))))
fig <- fig %>%
  layout(
         xaxis = list(zerolinecolor = '#ffff',
                      zerolinewidth = 2,
                      gridcolor = 'ffff'),
         yaxis = list(zerolinecolor = '#ffff',
                      zerolinewidth = 2,
                      gridcolor = 'ffff'),
         plot_bgcolor='#e5ecf6', margin = 0.1, width = 900)
fig

5.2 Candlestick Charts

5.2.1 Basic Candlestick

library(plotly)
library(quantmod)

getSymbols("AAPL",src='yahoo')
# basic example of ohlc charts
df <- data.frame(Date=index(AAPL),coredata(AAPL))
df <- tail(df, 30)

fig <- df %>% plot_ly(x = ~Date, type="candlestick",
          open = ~AAPL.Open, close = ~AAPL.Close,
          high = ~AAPL.High, low = ~AAPL.Low) 
fig <- fig %>% layout(title = "Basic Candlestick Chart")

fig

## [1] "AAPL"

5.2.2 Customise the fig ure with Shapes and Annotations

library(plotly)
library(quantmod)

getSymbols("AAPL",src='yahoo')
df <- data.frame(Date=index(AAPL),coredata(AAPL))

# annotation
a <- list(text = "Stock Split",
          x = '2014-06-06',
          y = 1.02,
          xref = 'x',
          yref = 'paper',
          xanchor = 'left',
          showarrow = FALSE
)

# use shapes to create a line
l <- list(type = line,
          x0 = '2014-06-06',
          x1 = '2014-06-06',
          y0 = 0,
          y1 = 1,
          xref = 'x',
          yref = 'paper',
          line = list(color = 'black',
                      width = 0.5)
)

fig <- df %>% plot_ly(x = ~Date, type="candlestick",
          open = ~AAPL.Open, close = ~AAPL.Close,
          high = ~AAPL.High, low = ~AAPL.Low) 
fig <- fig %>% layout(title = "Apple Stock",
         annotations = a,
         shapes = l)

fig

## [1] "AAPL"

5.3 OHLC Charts

5.3.1 Basic OHLC Chart

library(plotly)
library(quantmod)

getSymbols("AAPL",src='yahoo')
df <- data.frame(Date=index(AAPL),coredata(AAPL))
df <- tail(df, 30)

fig <- df %>% plot_ly(x = ~Date, type="ohlc",
          open = ~AAPL.Open, close = ~AAPL.Close,
          high = ~AAPL.High, low = ~AAPL.Low) 
fig <- fig %>% layout(title = "Basic OHLC Chart",
         xaxis = list(rangeslider = list(visible = F)))

fig

## [1] "AAPL"

5.3.2 Customise the Figure with Shapes and Annotations

library(plotly)
library(quantmod)

getSymbols("AAPL",src='yahoo')
df <- data.frame(Date=index(AAPL),coredata(AAPL))

# annotation
a <- list(text = "Stock Split",
          x = '2014-06-06',
          y = 1.02,
          xref = 'x',
          yref = 'paper',
          xanchor = 'left',
          showarrow = FALSE
          )

# use shapes to create a line
l <- list(type = line,
          x0 = '2014-06-06',
          x1 = '2014-06-06',
          y0 = 0,
          y1 = 1,
          xref = 'x',
          yref = 'paper',
          line = list(color = 'black',
                      width = 0.5)
          )

fig <- df %>% plot_ly(x = ~Date, type="ohlc",
          open = ~AAPL.Open, close = ~AAPL.Close,
          high = ~AAPL.High, low = ~AAPL.Low) 
fig <- fig %>% layout(title = "Custom Colors",
         annotations = a,
         shapes = l)

fig

## [1] "AAPL"

5.4 Waterfall Charts

5.4.1 Basic Waterfall Chart

library(plotly)

x= list("Sales", "Consulting", "Net revenue", "Purchases", "Other expenses", "Profit before tax")
measure= c("relative", "relative", "total", "relative", "relative", "total")
text= c("+60", "+80", "", "-40", "-20", "Total")
y= c(60, 80, 0, -40, -20, 0)
data = data.frame(x=factor(x,levels=x),measure,text,y)

fig <- plot_ly(
  data, name = "20", type = "waterfall", measure = ~measure,
  x = ~x, textposition = "outside", y= ~y, text =~text,
  connector = list(line = list(color= "rgb(63, 63, 63)"))) 
fig <- fig %>%
  layout(title = "Profit and loss statement 2018",
        xaxis = list(title = ""),
        yaxis = list(title = ""),
        autosize = TRUE,
        showlegend = TRUE)

fig

5.5 Funnel Charts

# Need to install plotly from Github to get funnel plots
devtools::install_github("ropensci/plotly")

5.5.1 Basic Funnel Plot

library(plotly)

fig <- plot_ly() 
fig <- fig %>%
  add_trace(
  type = "funnel",
  y = c("Website visit", "Downloads", "Potential customers", "Requested price", "invoice sent"),
  x = c(39, 27.4, 20.6, 11, 2)) 
fig <- fig %>%
  layout(yaxis = list(categoryarray = c("Website visit", "Downloads", "Potential customers", "Requested price", "invoice sent")))

fig

5.5.2 Stacked Funnel Plot

library(plotly)

fig <- plot_ly(
    type = "funnel",
    name = 'Montreal',
    y = c("Website visit", "Downloads", "Potential customers", "Requested price"),
    x = c(120, 60, 30, 20),
    textinfo = "value+percent initial") 
fig <- fig %>%
  add_trace(
    type = "funnel",
    name = 'Toronto',
    orientation = "h",
    y = c("Website visit", "Downloads", "Potential customers", "Requested price", "invoice sent"),
    x = c(100, 60, 40, 30, 20),
    textposition = "inside",
    textinfo = "value+percent previous") 
fig <- fig %>%
  add_trace(
    type = "funnel",
    name = 'Vancouver',
    orientation = "h",
    y = c("Website visit", "Downloads", "Potential customers", "Requested price", "invoice sent", "Finalized"),
  x = c(90, 70, 50, 30, 10, 5),
  textposition = "outside",
  textinfo = "value+percent total") 
fig <- fig %>%
  layout(yaxis = list(categoryarray = c("Website visit", "Downloads", "Potential customers", "Requested price", "invoice sent", "Finalized")))

fig

5.5.3 Basic Area Funnel Plot

library(plotly)

fig <- plot_ly(
  type = "funnelarea",
  text = c("The 1st","The 2nd", "The 3rd", "The 4th", "The 5th"),
  values = c(5, 4, 3, 2, 1))

fig

6 Maps

6.1 Choropleth Maps

6.1.1 Choropleth Map Using GeoJSON

library(plotly)
library(rjson)

url <- 'https://raw.githubusercontent.com/plotly/datasets/master/geojson-counties-fips.json'
counties <- rjson::fromJSON(file=url)
url2<- "https://raw.githubusercontent.com/plotly/datasets/master/fips-unemp-16.csv"
df <- read.csv(url2, colClasses=c(fips="character"))
g <- list(
  scope = 'usa',
  projection = list(type = 'albers usa'),
  showlakes = TRUE,
  lakecolor = toRGB('white')
)
fig <- plot_ly()
fig <- fig %>% add_trace(
    type="choropleth",
    geojson=counties,
    locations=df$fips,
    z=df$unemp,
    colorscale="Viridis",
    zmin=0,
    zmax=12,
    marker=list(line=list(
      width=0)
    )
  )
fig <- fig %>% colorbar(title = "Unemployment Rate (%)")
fig <- fig %>% layout(
    title = "2016 US Unemployment by County"
)

fig <- fig %>% layout(
    geo = g
  )

fig

6.1.2 Indexing by GeoJSON Properties

library(plotly)
library(rjson)

url <- 'https://raw.githubusercontent.com/plotly/datasets/master/election.geojson'
geojson <- rjson::fromJSON(file=url)
url2<- "https://raw.githubusercontent.com/plotly/datasets/master/election.csv"
df <- read.csv(url2)
g <- list(
  fitbounds = "locations",
  visible = FALSE
)
fig <- plot_ly() 
fig <- fig %>% add_trace(
    type="choropleth",
    geojson=geojson,
    locations=df$district,
    z=df$Bergeron,
    colorscale="Viridis",
    featureidkey="properties.district"
  )
fig <- fig %>% layout(
    geo = g
  )
fig <- fig %>% colorbar(title = "Bergeron Votes")
fig <- fig %>% layout(
    title = "2013 Montreal Election"
)
fig

6.1.3 Customize choropleth chart

library(plotly)
df <- read.csv("https://raw.githubusercontent.com/plotly/datasets/master/2011_us_ag_exports.csv")
df$hover <- with(df, paste(state, '<br>', "Beef", beef, "Dairy", dairy, "<br>",
                           "Fruits", total.fruits, "Veggies", total.veggies,
                           "<br>", "Wheat", wheat, "Corn", corn))
# give state boundaries a white border
l <- list(color = toRGB("white"), width = 2)
# specify some map projection/options
g <- list(
  scope = 'usa',
  projection = list(type = 'albers usa'),
  showlakes = TRUE,
  lakecolor = toRGB('white')
)

fig <- plot_geo(df, locationmode = 'USA-states')
fig <- fig %>% add_trace(
    z = ~total.exports, text = ~hover, locations = ~code,
    color = ~total.exports, colors = 'Purples'
  )
fig <- fig %>% colorbar(title = "Millions USD")
fig <- fig %>% layout(
    title = '2011 US Agriculture Exports by State<br>(Hover for breakdown)',
    geo = g
  )

fig

6.1.4 World Choropleth Map

df <- read.csv('https://raw.githubusercontent.com/plotly/datasets/master/2014_world_gdp_with_codes.csv')

# light grey boundaries
l <- list(color = toRGB("grey"), width = 0.5)

# specify map projection/options
g <- list(
  showframe = FALSE,
  showcoastlines = FALSE,
  projection = list(type = 'Mercator')
)

fig <- plot_geo(df)
fig <- fig %>% add_trace(
    z = ~GDP..BILLIONS., color = ~GDP..BILLIONS., colors = 'Blues',
    text = ~COUNTRY, locations = ~CODE, marker = list(line = l)
  )
fig <- fig %>% colorbar(title = 'GDP Billions US$', tickprefix = '$')
fig <- fig %>% layout(
    title = '2014 Global GDP<br>Source:<a href="https://www.cia.gov/library/publications/the-world-factbook/fields/2195.html">CIA World Factbook</a>',
    geo = g
  )

fig

6.2 Scatter Plots on Maps

6.2.1 Basic Scatter on Map

library(plotly)
df <- read.csv('https://raw.githubusercontent.com/plotly/datasets/master/2011_february_us_airport_traffic.csv')

# geo styling
g <- list(
  scope = 'usa',
  projection = list(type = 'albers usa'),
  showland = TRUE,
  landcolor = toRGB("gray95"),
  subunitcolor = toRGB("gray85"),
  countrycolor = toRGB("gray85"),
  countrywidth = 0.5,
  subunitwidth = 0.5
)

fig <- plot_geo(df, lat = ~lat, lon = ~long)
fig <- fig %>% add_markers(
    text = ~paste(airport, city, state, paste("Arrivals:", cnt), sep = "<br />"),
    color = ~cnt, symbol = I("square"), size = I(8), hoverinfo = "text"
  )
fig <- fig %>% colorbar(title = "Incoming flights<br />February 2011")
fig <- fig %>% layout(
    title = 'Most trafficked US airports<br />(Hover for airport)', geo = g
  )

fig

6.2.2 Style Scatter Map Layout

library(plotly)
df <- read.csv('https://raw.githubusercontent.com/plotly/datasets/master/2015_06_30_precipitation.csv')

# change default color scale title
m <- list(colorbar = list(title = "Total Inches"))

# geo styling
g <- list(
  scope = 'north america',
  showland = TRUE,
  landcolor = toRGB("grey83"),
  subunitcolor = toRGB("white"),
  countrycolor = toRGB("white"),
  showlakes = TRUE,
  lakecolor = toRGB("white"),
  showsubunits = TRUE,
  showcountries = TRUE,
  resolution = 50,
  projection = list(
    type = 'conic conformal',
    rotation = list(lon = -100)
  ),
  lonaxis = list(
    showgrid = TRUE,
    gridwidth = 0.5,
    range = c(-140, -55),
    dtick = 5
  ),
  lataxis = list(
    showgrid = TRUE,
    gridwidth = 0.5,
    range = c(20, 60),
    dtick = 5
  )
)

fig <- plot_geo(df, lat = ~Lat, lon = ~Lon, color = ~Globvalue)
fig <- fig %>% add_markers(
    text = ~paste(df$Globvalue, "inches"), hoverinfo = "text"
  )
fig <- fig %>% layout(title = 'US Precipitation 06-30-2015<br>Source: NOAA', geo = g)

fig

6.3 Mapbox Density

6.3.1 Mapbox Access Token

library(plotly)

quakes = read.csv('https://raw.githubusercontent.com/plotly/datasets/master/earthquakes-23k.csv')

fig <- quakes 
fig <- fig %>%
  plot_ly(
    type = 'densitymapbox',
    lat = ~Latitude,
    lon = ~Longitude,
    coloraxis = 'coloraxis',
    radius = 10) 
fig <- fig %>%
  layout(
    mapbox = list(
      style="stamen-terrain",
      center= list(lon=180)), coloraxis = list(colorscale = "Viridis"))

fig

6.4 Mapbox Layers

6.4.1 OpenStreetMap Tiles, no Token Needed

library(plotly)

us_cities = read.csv("https://raw.githubusercontent.com/plotly/datasets/master/us-cities-top-1k.csv")

fig <- us_cities 
fig <- fig %>%
  plot_ly(
    lat = ~lat,
    lon = ~lon,
    marker = list(color = "fuchsia"),
    type = 'scattermapbox',
    hovertext = us_cities[,"City"]) 
fig <- fig %>%
  layout(
    mapbox = list(
      style = 'open-street-map',
      zoom =2.5,
      center = list(lon = -88, lat = 34))) 

fig

6.4.2 Base Map Below A Trace: No Token Needed

library(plotly)

us_cities = read.csv("https://raw.githubusercontent.com/plotly/datasets/master/us-cities-top-1k.csv")

fig <- us_cities 
fig <- fig %>%
  plot_ly(
    lat = ~lat,
    lon = ~lon,
    type = "scattermapbox",
    hovertext = us_cities[,"City"],
    marker = list(color = "fuchsia")) 
fig <- fig %>%
  layout(mapbox= list(
    style = "white-bg",
    zoom = 3,
    center = list(lon = -93 ,lat= 41),
    layers = list(list(
      below = 'traces',
      sourcetype = "raster",
      source = list("https://basemap.nationalmap.gov/arcgis/rest/services/USGSImageryOnly/MapServer/tile/{z}/{y}/{x}")))))

fig

6.5 Bubble Maps

6.5.1 United States Bubble Map

library(plotly)
df <- read.csv('https://raw.githubusercontent.com/plotly/datasets/master/2014_us_cities.csv')

df$q <- with(df, cut(pop, quantile(pop)))
levels(df$q) <- paste(c("1st", "2nd", "3rd", "4th", "5th"), "Quantile")
df$q <- as.ordered(df$q)

g <- list(
  scope = 'usa',
  projection = list(type = 'albers usa'),
  showland = TRUE,
  landcolor = toRGB("gray85"),
  subunitwidth = 1,
  countrywidth = 1,
  subunitcolor = toRGB("white"),
  countrycolor = toRGB("white")
)

fig <- plot_geo(df, locationmode = 'USA-states', sizes = c(1, 250))
fig <- fig %>% add_markers(
    x = ~lon, y = ~lat, size = ~pop, color = ~q, hoverinfo = "text",
    text = ~paste(df$name, "<br />", df$pop/1e6, " million")
  )
fig <- fig %>% layout(title = '2014 US city populations<br>(Click legend to toggle)', geo = g)

fig

7 Artificial Intelligence and Machine Learning

8 3D Charts

8.0.1 Basic 3D Scatter Plot

library(plotly)

mtcars$am[which(mtcars$am == 0)] <- 'Automatic'
mtcars$am[which(mtcars$am == 1)] <- 'Manual'
mtcars$am <- as.factor(mtcars$am)

fig <- plot_ly(mtcars, x = ~wt, y = ~hp, z = ~qsec, color = ~am, colors = c('pink', 'blue'))
fig <- fig %>% add_markers(size = 10)
fig <- fig %>% layout(scene = list(xaxis = list(title = 'Weight'),
                     yaxis = list(title = 'Gross horsepower'),
                     zaxis = list(title = '1/4 mile time')))

fig

8.0.2 Basic 3D Surface Plot

# volcano is a numeric matrix that ships with R
fig <- plot_ly(z = ~volcano)
fig <- fig %>% add_surface()

fig

9 Transforms

9.1 Filter

9.1.1 Basic Example

library(plotly)

fig <- plot_ly(
  type = 'scatter',
  x = mtcars$hp,
  y = mtcars$qsec,
  text = rownames(mtcars),
  hoverinfo = 'text',
  mode = 'markers',
  transforms = list(
    list(
      type = 'filter',
      target = 'y',
      operation = '>',
      value = mean(mtcars$qsec)
    )
  )
)

fig

9.2 Group

9.2.1 Basic Example

library(plotly)

fig <- plot_ly(
  type = 'scatter',
  x = mtcars$hp,
  y = mtcars$qsec,
  text = paste("Make: ", rownames(mtcars),
               "<br>hp: ", mtcars$hp,
               "<br>qsec: ", mtcars$qsec,
               "<br>Cyl: ", mtcars$cyl),
  hoverinfo = 'text',
  mode = 'markers',
  transforms = list(
    list(
      type = 'groupby',
      groups = mtcars$cyl,
      styles = list(
        list(target = 4, value = list(marker =list(color = 'blue'))),
        list(target = 6, value = list(marker =list(color = 'red'))),
        list(target = 8, value = list(marker =list(color = 'black')))
      )
      )
    )
  )

fig

9.3 Aggregation

9.3.1 Basic Example

library(plotly)

fig <- plot_ly(
  type = 'scatter',
  x = diamonds$cut,
  y = diamonds$price,
  mode = 'markers',
  transforms = list(
    list(
      type = 'aggregate',
      groups = diamonds$cut,
      aggregations = list(
        list(
          target = 'y', func = 'sum', enabled = T
        )
      )
    )
  )
)

fig

9.3.2 Histogram Binning

library(plotly)

df <- read.csv("https://plotly.com/~public.health/17.csv", skipNul = TRUE, encoding = "UTF-8")

labels <- function(size, label) {
  list(
    args = c("xbins.size", size), 
    label = label, 
    method = "restyle"
  )
}

fig <- df %>%
  plot_ly(
    x = ~date,
    autobinx = FALSE, 
    autobiny = TRUE, 
    marker = list(color = "rgb(68, 68, 68)"), 
    name = "date", 
    type = "histogram", 
    xbins = list(
      end = "2016-12-31 12:00", 
      size = "M1", 
      start = "1983-12-31 12:00"
    )
  )
fig <- fig %>% layout(
  paper_bgcolor = "rgb(240, 240, 240)", 
  plot_bgcolor = "rgb(240, 240, 240)", 
  title = "<b>Shooting Incidents</b><br>use dropdown to change bin size",
  xaxis = list(
    type = 'date'
  ),
  yaxis = list(
    title = "Incidents"
  ),
  updatemenus = list(
    list(
      x = 0.1, 
      y = 1.15,
      active = 1, 
      showactive = TRUE,
      buttons = list(
        labels("D1", "Day"),
        labels("M1", "Month"),
        labels("M6", "Half Year"),
        labels("M12", "Year")
      )
    )
  )
)

fig

9.3.3 Mapping with Aggregations

library(plotly)

df <- read.csv("https://raw.githubusercontent.com/bcdunbar/datasets/master/worldhappiness.csv")

s <- schema()
agg <- s$transforms$aggregate$attributes$aggregations$items$aggregation$func$values


l = list()
for (i in 1:length(agg)) {
  ll = list(method = "restyle",
            args = list('transforms[0].aggregations[0].func', agg[i]),
            label = agg[i]) 
  l[[i]] = ll
}

fig <- df %>%
  plot_ly(
    type = 'choropleth',
    locationmode = 'country names',
    locations = ~Country,
    z = ~HappinessScore,
    autocolorscale = F,
    reversescale = T,
    colorscale = 'Portland', 
    transforms = list(list(
      type = 'aggregate',
      groups = ~Country,
      aggregations = list(
        list(target = 'z', func = 'sum', enabled = T)
      )
    ))
  )
fig <- fig %>% layout(
    title = "<b>World Happiness</b>",
    geo = list(
      showframe = F,
      showcoastlines = F
    ),
    updatemenus = list(
      list(
        x = 0.25,
        y = 1.04,
        xref = 'paper',
        yref = 'paper',
        yanchor = 'top',
        buttons = l
      )
    )
  )

fig

10 Animations